AI Article Synopsis

  • - X-linked myotubular myopathy (XLMTM) is a rare genetic condition that mainly affects newborns and infants, leading to high mortality due to severe muscle weakness and breathing issues, though some patients may have milder cases and survive into adulthood with noticeable facial differences.
  • - The study examined genetic variations in XLMTM patients to find connections between their specific gene mutations and the severity of their symptoms, studying a cohort of 414 patients and using facial recognition technology (Face2Gene) to analyze distinguishable facial traits between patients and healthy individuals.
  • - Results indicated severe symptoms were linked to certain serious genetic mutations, while milder cases were associated with less severe mutations; additionally, Face2Gene successfully identified differences

Article Abstract

Background: X-linked myotubular myopathy (XLMTM) is a rare congenital myopathy resulting from dysfunction of the protein myotubularin encoded by the gene. XLMTM has a high neonatal and infantile mortality rate due to a severe myopathic phenotype and respiratory failure. However, in a minority of XLMTM cases, patients present with milder phenotypes and achieve ambulation and adulthood. Notable facial dysmorphia is also present.

Methods: We investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients' cohort (previously published data plus three novel variants, = 414). Based on the facial gestalt difference between XLMTM patients and unaffected controls, we investigated the use of the Face2Gene application.

Results: Significant associations between severe phenotype and truncating variants ( < 0.001), frameshift variants ( < 0.001), nonsense variants ( = 0.006), and in/del variants ( = 0.036) were present. Missense variants were significantly associated with the mild and moderate phenotype ( < 0.001). The Face2Gene application showed a significant difference between XLMTM patients and unaffected controls ( = 0.001).

Conclusions: Using genotype-phenotype correlations could predict the disease course in most XLMTM patients, but still with limitations. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM using the correct algorithm.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10742680PMC
http://dx.doi.org/10.3390/genes14122174DOI Listing

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